Dairy Science Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden, NSW 2567, Australia; NSW Department of Primary Industries, Menangle, NSW 2568, Australia.
NSW Department of Primary Industries, Menangle, NSW 2568, Australia.
Animal. 2022 Sep;16(9):100605. doi: 10.1016/j.animal.2022.100605. Epub 2022 Aug 9.
There is a large variability in profitability and productivity between farms operating with automatic milking systems (AMS). The objectives of this study were to identify the physical factors associated with profitability and productivity of pasture-based AMS and quantify how changes in these factors would affect farm productivity. We utilised two different datasets collected between 2015 and 2019 with information from commercial pasture-based AMS farms. One contained annual physical and economic data from 14 AMS farms located in the main Australian dairy regions; the other contained monthly, detailed robot-system performance data from 23 AMS farms located across Australia, Ireland, New Zealand, and Chile. We used linear mixed models to identify the physical factors associated with different profitability (Model 1) and partial productivity measures (Model 2). Additionally, we conducted a Monte Carlo simulation to evaluate how changes in the physical factors would affect productivity. Our results from Model 1 showed that the two main factors associated with profitability in pasture-based AMS were milk harvested/robot (MH; kg milk/robot per day) and total labour on-farm (full-time equivalent). On average, Model 1 explained 69% of the variance in profitability. In turn, Model 2 showed that the main factors associated with MH were cows/robot, milk flow, milking frequency, milking time, and days in milk. Model 2 explained 90% of the variance in MH. The Monte Carlo simulation showed that if pasture-based AMS farms manage to increase the number of cows/robot from 54 (current average) to ∼ 70 (the average of the 25% highest performing farms), the probability of achieving high MH, and therefore profitability, would increase from 23% to 63%. This could make AMS more attractive for pasture-based systems and increase the rate of adoption of the technology.
采用自动化挤奶系统（AMS）的牧场经济效益和生产效率存在较大差异。本研究旨在确定与基于 pasture-based AMS 的盈利能力和生产力相关的物理因素，并量化这些因素的变化将如何影响农场的生产力。我们利用了两个不同的数据集，这些数据集是在 2015 年至 2019 年间收集的，包含了来自商业 pasture-based AMS 农场的信息。一个包含了位于澳大利亚主要乳制品产区的 14 个 AMS 农场的年度物理和经济数据；另一个包含了来自澳大利亚、爱尔兰、新西兰和智利的 23 个 AMS 农场的月度、详细的机器人系统性能数据。我们使用线性混合模型来确定与不同盈利能力（模型 1）和部分生产力衡量标准（模型 2）相关的物理因素。此外，我们还进行了蒙特卡罗模拟，以评估物理因素的变化将如何影响生产力。模型 1 的结果表明，与 pasture-based AMS 的盈利能力相关的两个主要因素是每台机器人挤奶量（MH；每天每台机器人的牛奶产量）和农场总劳动力（全职当量）。平均而言，模型 1 解释了盈利能力差异的 69%。反过来，模型 2 表明与 MH 相关的主要因素是每台机器人的奶牛数量、牛奶流量、挤奶频率、挤奶时间和泌乳天数。模型 2 解释了 MH 方差的 90%。蒙特卡罗模拟表明，如果 pasture-based AMS 农场设法将每台机器人的奶牛数量从 54 头（目前的平均水平）增加到约 70 头（25%表现最好的农场的平均水平），那么实现高 MH，从而提高盈利能力的可能性将从 23%增加到 63%。这可能使 AMS 对基于 pasture-based 的系统更具吸引力，并提高该技术的采用率。
